Key Responsibilities :

1. Architectural Strategy & Design :

- Architect and scale modern data solutions (ETL/ELT pipelines, data lakes, and data warehouses) across multiple major cloud platforms like Databricks, Snowflake, BigQuery, or Microsoft Fabric.

- Define standard patterns for integrating operational application layers with analytical data systems seamlessly.

2. Hands-on Execution & Engineering Excellence :

- Maintain a hybrid approach by remaining hands-on with architecture blueprints, system design, prototyping, and high-impact code reviews.

- Lead by example to champion clean code, robust CI/CD pipelines, containerization (Kubernetes/Docker), and reliable distributed systems.

- Troubleshoot complex architectural bottlenecks across application and data layers.

3. AI & Intelligent Automation :

- Drive the production deployment of Agentic AI frameworks, autonomous workflows, and sophisticated prompt engineering protocols.

- Architect robust Retrieval-Augmented Generation (RAG) pipelines that bridge enterprise application microservices with large-scale analytical data stores safely and securely.

4. Technical Leadership & Calibration :

- Mentor, guide, and upskill senior engineers, tech leads, and software architects.

- Collaborate closely with Practice Owners and Business Leaders to align engineering capabilities with client demands and emerging technology trends.

Technical Requirements & Qualifications :

Core Experience :

- 12 - 15+ years of progressive experience in software engineering and enterprise architecture.

- Proven track record of operating at a Director, Principal Architect, or Chief Architect level, managing complex multi-system environments.

Application Engineering & Frameworks :

- Advanced, deep-dive expertise in .NET Core and/or Java/Spring Boot.

- Extensive hands-on experience with Microservices architectures, domain-driven design (DDD), and distributed systems.

- Deep knowledge of Event-Driven Frameworks and messaging infrastructure (e.g., Kafka, RabbitMQ, AWS Kinesis).

Enterprise Data Ecosystems :

- Deep, practical knowledge of Enterprise Data Architecture, data modeling, and robust ETL/ELT engineering.

- Production-grade, hands-on experience in at least 2 - 3 of the following modern platforms : Databricks (Delta Lake, Unity Catalog, Spark), Snowflake (Snowpark, Streamlit, Data Sharing), Google BigQuery, or Microsoft Fabric.

Generative AI & Automation :

- Foundational or production experience deploying Agentic AI systems (e.g., LangChain, AutoGen, CrewAI, or semantic kernels).

- Strong capability in structured prompt engineering, vector database implementation (e.g., Pinecone, Milvus, pgvector), and securing LLM-orchestrated applications.